Jump to ContentJump to Main Navigation
Show Summary Details
More options …

International Journal of Computer Science in Sport

2 Issues per year


CiteScore 2016: 0.40

SCImago Journal Rank (SJR) 2015: 0.154
Source Normalized Impact per Paper (SNIP) 2015: 0.359

Open Access
Online
ISSN
1684-4769
See all formats and pricing
More options …

Computational Estimation of Football Player Wages

L. Yaldo / L. Shamir
Published Online: 2017-07-22 | DOI: https://doi.org/10.1515/ijcss-2017-0002

Abstract

The wage of a football player is a function of numerous aspects such as the player’s skills, performance in the previous seasons, age, trajectory of improvement, personality, and more. Based on these aspects, salaries of football players are determined through negotiation between the team management and the agents. In this study we propose an objective quantitative method to determine football players’ wages based on their skills. The method is based on the application of pattern recognition algorithms to performance (e.g., scoring), behavior (e.g., aggression), and abilities (e.g., acceleration) data of football players. Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). The proposed method can be used as an assistive technology when negotiating players salaries, as well as for performing quantitative analysis of links between the salary and the performance of football players. The method is based on the performance and skills of the players, but does not take into account aspects that are not related directly to the game such as the popularity of the player among fans, predicted merchandise sales, etc, which are also factors of high impact on the salary, especially in the case of the team lead players and superstars. Analysis of player salaries in eight European football leagues show that the skills that mostly affect the salary are largely consistent across leagues, but some differences exist. Analysis of underpaid and overpaid players shows that overpaid players tend to be stronger, but are inferior in their reactions, vision, acceleration, agility, and balance compared to underpaid football players.

Keywords: football; soccer; sports economy; machine learning

References

  • Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1):37–66.CrossrefGoogle Scholar

  • Aldous, D. (1993). The continuum random tree III. The Annals of Probability, 248–289.CrossrefGoogle Scholar

  • Arnedt, R. B. (1998). European union law and football nationality restrictions: the economics and politics of the bosman decision. Emory International Law Review, 12, 1091.Google Scholar

  • Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning for control. In Lazy learning (pp. 75-113). Springer Netherlands.Google Scholar

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Machine Learning, 128, 1–58.Google Scholar

  • Bryson, A., Rossi, G., & Simmons, R. (2014). The migrant wage premium in professional football: a superstar effect? Kyklos, 67(1), 12–28.Web of ScienceCrossrefGoogle Scholar

  • Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). Evaluation of research using computerised tracking systems (amisco r and prozone r) to analyse physical performance in elite soccer: A systematic review. Sports Medicine, 44(5), 701–712.CrossrefGoogle Scholar

  • Cleary, J. G., Trigg, L. E., et al. (1995). K*: An instance-based learner using an entropic distance measure. In Proceedings of the 12th International Conference on Machine learning, 5, 108–114.Google Scholar

  • Dasarathy, B. V. (1994). Minimal consistent set (mcs) identification for optimal nearest neighbor decision systems design. IEEE Transactions on Systems, Man, and Cybernetics, 24(3), 511–517.Google Scholar

  • Dejonghe, T. & Van Opstal, W. (2010). Competitive balance between national leagues in european football after the bosman case. Rivista di Diritto ed Economia dello Sport, 6(2), 41–61.Google Scholar

  • Feess, E., Gerfin, M., & Muehlheusser, G. (2010). The incentive effects of long-term contracts on performance-evidence from a natural experiment in european soccer. Technical Report, Mimeo: Berlin.Google Scholar

  • Frank, E., Hall, M., & Pfahringer, B. (2002). Locally weighted naive bayes. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 249–256.Google Scholar

  • Frick, B. (2006). Salary determination and the pay-performance relationship in professional soccer: Evidence from germany. Sports Economics After Fifty Years: Essays in Honour of Simon Rottenberg. Oviedo: Ediciones de la Universidad de Oviedo, 125–146.Google Scholar

  • Frick, B. (2007). The football player’s labor market: Empirical evidence from the major european leagues. Scottish Journal of Political Economy, 54(3), 422–446.CrossrefGoogle Scholar

  • Frick, B. (2011). Performance, salaries, and contract length: empirical evidence from german soccer. International Journal of Sport Finance, 6(2), 87.Google Scholar

  • Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.CrossrefGoogle Scholar

  • Garcia-del Barrio, P., & Pujol, F. (2007). Pay and performance in the spanish soccer league: who gets the expected monopsony rents. Technical report, University of Navarra, SpainGoogle Scholar

  • Garcia-del Barrio, P., & Pujol, F. (2009). The rationality of under-employing the bestperforming soccer players. Labour, 23(3), 397–419.Google Scholar

  • Giulianotti, R. (2012). Football. Wiley Online Library.Google Scholar

  • Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.CrossrefGoogle Scholar

  • Jensen, M. M., Grønbæk, K., Thomassen, N., Andersen, J., and Nielsen, J. (2014). Interactive football-training based on rebounders with hit position sensing and audio-visual feedback. Intentional Journal of Computer Science in Sport, 13(1), 57–68.Google Scholar

  • Kase, K., De Hoyos, I. U., Sanchis, C. M., & Breton, M. O. (2007). The proto-image of real madrid: implications for marketing and management. International Journal of Sports Marketing and Sponsorship, 8(3), 7–28.Google Scholar

  • Kohavi, R. (1995). The power of decision tables. In European Conference on Machine Learning, 174–189.Google Scholar

  • Lames, M., McGarry, T., Nebel, B., & Roemer, K. (2011). Computer science in sportspecial emphasis: Football (dagstuhl seminar 11271). Dagstuhl Reports, 1(7).Google Scholar

  • Markovits, A. S., & Green, A. I. (2017). FIFA, the video game: a major vehicle for soccer’s popularization in the United States. Sport in Society, 20(5-6), 716-734.Google Scholar

  • Muller, J. C., Lammert, J., & Hovemann, G. (2012). The financial fair play regulations of uefa: An adequate concept to ensure the long-term viability and sustainability of european club football? International Journal of Sport Finance, 7(2), 117.Google Scholar

  • O’Donoghue, P. & Robinson, G. (2009). Validity of the prozone3 r player tracking system: A preliminary report. International Journal of Computer Science in Sport, 8(1), 37–53.Google Scholar

  • Orejan, J. (2011). Football/Soccer: History and tactics. McFarland. Jefferson, NC, USA.Google Scholar

  • Prasetio, D. (2016). Predicting football match results with logistic regression. In International Conference on Advanced Informatics: Concepts, Theory And Application, 1–5.Google Scholar

  • Robnik-Sikonja, M. & Kononenko, I. (1997). An adaptation of relief for attribute estimation in regression. In Machine Learning: Proceedings of the Fourteenth International Conference, 296–304.Google Scholar

  • Rohde, M. & Breuer, C. (2016). Europes elite football: Financial growth, sporting success, transfer investment, and private majority investors. International Journal of Financial Studies, 4(2), 12.Google Scholar

  • Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 287–294.Google Scholar

  • Shin, J. & Gasparyan, R. (2014). A novel way to soccer match prediction. Technical Report, Stanford U., CA. USA.Google Scholar

  • Siegle, M., Stevens, T., & Lames, M. (2013). Design of an accuracy study for position detection in football. Journal of Sports Sciences, 31(2), 166–172.Web of ScienceCrossrefGoogle Scholar

  • Torgler, B. & Schmidt, S. L. (2007). What shapes player performance in soccer? empirical findings from a panel analysis. Applied Economics, 39(18), 2355–2369.CrossrefGoogle Scholar

  • Torgler, B., Schmidt, S. L., & Frey, B. S. (2006). Relative income position and performance: an empirical panel analysis.Google Scholar

  • Wicker, P., Prinz, J., Weimar, D., Deutscher, C., & Upmann, T. (2013). No pain, no gain? effort and productivity in professional soccer. International Journal of Sport Finance, 8(2), 124.Google Scholar

About the article

Published Online: 2017-07-22

Published in Print: 2017-07-01


Citation Information: International Journal of Computer Science in Sport, ISSN (Online) 1684-4769, DOI: https://doi.org/10.1515/ijcss-2017-0002.

Export Citation

© 2017 Yaldo, L. et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in